1. Application of artificial neural network in materials research Wei SHA Professor of Materials Science http://space.qub.ac.uk:8077/cber/Sha

2. The models Integrated Part III (pp. 301-410) “Application of neural- network models” pp. 553-565

3. Composition-processing-temperature-mechanical properties TTT diagrams Fatigue life CCT diagrams Microhardness profile The models Graphical user interfaces of software for modelling

4. Basic principles of neural network modelling NatualArtificial Input Layer Hidden Layer Output Layer Neuron Neural Network The human brain contains 10 10 – 10 11 neurons

5. Basic principles of neural network modelling Steps Database collection Analysis and pre-processing of the data Design and training of the neural network Test of the trained network Using the trained NN for simulation and prediction

6. Database construction and analysis Distribution of the input dataset

7. Reading of file with database Normalisation of the data Creating neural network and defining training parameters Neural network training Post-training analyses for training and test subsets Use of the model Experimental verification Random redistribution of database Dividing to training and test subsets for inputs and corresponding outputs Loop for new random redistribution of the database Loops for new network architecture, training algorithm, transfer function and training parameters Algorithm of computer program Creation of neural network model

8. -40-30-20-10010203040 0 50 100 150 200 250 300 350 400 Number ×100 (%) HV Error EXP NNEXP - = Min = -37.94 Max = 35.89 Mean = -0.13 STDEV = 10.31 Algorithm of computer program Post-training validation of the software simulations

9. Algorithm of computer program Comparison between prediction and experiments

10. Use of the software Block diagram of software system Databases Computer program for training (learning) Trained artificial neural networks Graphical User Interfaces for use of the models Graphical User Interfaces for upgrade of the models Module for new data input Module for input of re- training parameters Module for materials selection

11. Use of the software Influence of alloy composition in  -TiAl, 1040 °C

12. Use of the software Microhardness profiles of titanium after nitriding

13. Use of the software Ti–15Mo–5Zr–3Al, nitrided in N 2 at 750 °C for 60 h (µm)

14. Use of the software Optimization of alloy composition and processing http://space.qub.ac.uk:8077/cber/Sha Optimisation Criteria Trained Neural Network Solution Loops for Heat Treatment Loops for Temperature Loops for Alloy Composition Find Alloy composition with max strength at 420°C Fix Heat treatment = Annealing T = 420°C Sn, Cr, Fe, Si, Nb, Mn = 0; O = 0.12 Vary Al, Mo, Zr, V Solution Al = 5.8; Mo = 7.3; Zr = 5.2; V = 0 Tensile strength (420°C) = 932 MPa; Yield strength = 665 MPa; Elongation = 10%; Modulus of elasticity = 94 GPa; Fatigue strength = 448 MPa; Fracture toughness = 101 MPa m 1/2